Generate some separated data and visualize it:

Construct a classifier function using the Automatic CriterionFunction:

Construct a classifier function using the Calinski–Harabasz index as CriterionFunction:

Compare the two clusterings of the data:

Create a ClassifierFunction from a list of images and classify new examples:

Create a custom FeatureExtractor to extract features:

Generate a classifier function and give a name to each feature:

Use the association format to assign cluster to a new example:

The list format can still be used:

Generate a classifier function assuming numerical and nominal feature types:

Generate a classifier function assuming nominal feature types instead:

Compare the result on new examples:

Generate some data using uniform distributions:

Classify the data:

Use Information to obtain a method description:

Look at the clustered data:

Classify the data using k-means:

Look at the clustered data:

Generate a large dataset using multinormal distributions and visualize it:

Use ClusterClassify to find clusters by specifying the method to use and look at the AbsoluteTiming:

Look at the resulting clustering:

Use ClusterClassify to find clusters without specifying the method to use and look at the AbsoluteTiming:

Generate a large dataset using multinormal distributions and visualize it:

Use ClusterClassify to find clusters:

Get the top cluster probabilities for a point with missing data:

Set the missing value synthesis to replace each missing variable with its estimated most likely value given known values (which is the default behavior):

Replace missing variables with random samples conditioned on known values:

Get the distribution of likely clusters for the point by replacing missing variables repeatedly with the random sampling strategy:

Generate a uniformly distributed dataset and visualize it:

Obtain a classifier from this data, with an emphasis on training speed:

Assign clusters to some randomly generated data and look at the AbsoluteTiming:

Obtain a classifier from this data, with an emphasis on the speed:

Assign clusters to some randomly generated data and look at the AbsoluteTiming compared to the one above:

Visualize the two clusterings for the test data and note how the setting "TrainingSpeed" gives better results:

Train several classifiers on random colors:

Compute the classifiers on a new color and observe that the result is always the same:

Train several classifiers on the same colors by using different values of the RandomSeeding option:

Compute the classifiers on and observe how the classifier differs:

Generate some separated data containing outliers:

Clusterize the data:

Use the classifier function to classify the outlier together with another point:

Clusterize the data, adding a big weight on the outlier:

Use the classifier function to classify the same points: